For researchers who wish to employ artificial intelligence to solve significant drug development difficulties, machine learning researcher jobs in San Francisco present an interesting possibility. Capable is looking for a gifted machine learning researcher to join its mission-driven team and create cutting-edge computational algorithms that can speed up and improve the process of finding new medications.
The advertised salary range for this full-time, on-site employment in San Francisco, California, is between $150,000 and $300,000 annually. The position integrates drug development, biomolecular modeling, protein design, machine learning research, and close coordination with wet-lab scientists.
Instead of focusing only on theoretical research, you will be able to compare your models and concepts to actual biological data and experimental findings. Researchers that love challenging scientific issues, independent thought, quick experimentation, and assuming responsibility from an original concept to a practical answer are especially well-suited for this role.
Details of Machine Learning Researcher Job:
- Job Title: Machine Learning Researcher Jobs in USA – Apply Now
- Hiring Company: Capable
- Location: San Francisco, California, USA
- Work Arrangement: On-site
- Employment Type: Full-time
- Salary: Approximately $150,000–$300,000 per year advertised
- Industry: Artificial Intelligence, Biotechnology, Computational Biology, Drug Discovery
- Visa Sponsorship: Available where appropriate
About Capable:
Capable is a science and technology company based in San Francisco with a strong focus on accelerating drug development through advanced computational methods.
The team includes people with backgrounds associated with organizations and institutions such as MIT, Harvard Medical School, Roche, ETH, and Dana-Farber. The company describes its environment as ambitious, rigorous, collaborative, and highly focused on meaningful outcomes.
Because Capable operates in an early-stage environment, researchers are expected to do more than follow established processes. Team members are encouraged to identify important problems, bring structure to uncertainty, test ideas quickly, learn from evidence, and take responsibility for the results.
For someone who wants to grow rapidly as a researcher while working on real-world scientific challenges, this type of environment can provide substantial learning and ownership.
What Does a Machine Learning Researcher Do at Capable?
The Machine Learning Researcher will work at the intersection of AI, computational biology, protein modeling, and drug discovery.
Your work may involve identifying limitations in the company’s drug discovery pipeline and designing machine learning approaches to address those challenges. You will also help build systems that allow scientists to use computational tools more effectively during research and development.
Key responsibilities include:
- Identifying high-impact bottlenecks throughout the drug discovery pipeline.
- Designing and developing targeted machine learning solutions for scientific problems.
- Building systems for experiment planning and scientific workflow support.
- Developing tools for literature triage and protocol drafting.
- Creating computational systems for in-silico candidate screening.
- Automating useful workflows across preclinical and clinical research.
- Working directly with wet-lab scientists and scientific operators.
- Applying protein and structure models such as ESM, AlphaFold-family models, RFdiffusion, and ProteinMPNN.
- Fine-tuning biological models using in-house datasets.
- Generating and optimizing potential candidates across research programs.
- Developing approaches for candidate generation, filtering, predictive modeling, and computational analysis.
- Exploring methods such as molecular dynamics and biomolecular model post-training.
- Designing evaluations to understand how machine learning systems perform in practical scientific settings.
- Using active learning to connect computational predictions with experimental and in-vivo results.
Skills and Qualifications:
Capable is looking for someone with strong research judgment, curiosity, technical ability, and a genuine interest in improving drug development.
A strong candidate may have experience with:
- Machine learning research
- Biomolecular modeling
- Protein structure modeling
- Computational drug discovery
- Biological datasets
- Active learning
- Predictive modeling
- Multimodal machine learning
- Scientific experimentation
- AI systems and research platforms
You should also be comfortable working in situations where the problem is not completely defined. The ability to decide what matters, formulate useful hypotheses, design experiments, interpret results, and communicate conclusions clearly is important.
Experience is valuable, but demonstrated research ability and strong project work can also help candidates stand out.
Preferred or Bonus Experience:
Candidates with experience or substantial project work involving the following areas may have an additional advantage:
- Active learning
- Biological modeling with limited data
- Omics data
- Imaging data
- Phenotypic datasets
- Multimodal biological models
- Production-scale AI agent platforms
- Computational protein design
- Drug discovery machine learning systems
Salary and Compensation
The advertised compensation range for this Machine Learning Researcher opportunity is approximately $150,000 to $300,000 per year.
The detailed job information also references a broader salary range of $130,000 to $300,000, with compensation depending on the candidate’s skills, experience, responsibilities, and expected impact.
The role may additionally include equity options with an indicated current value of approximately $50,000 to $200,000, before exercise costs, taxes, dilution, and liquidity considerations.
Candidates whose experience or compensation expectations fall outside the stated range may still be considered depending on the value and scope they could bring to the organization.
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Benefits of Machine Learning Researcher Jobs in USA:
- Competitive Salary and Equity Potential: Machine Learning Researcher Jobs in USA can offer highly competitive compensation, with this opportunity advertising approximately $150,000 to $300,000 annually alongside potential equity options.
- Meaningful Scientific Impact: Researchers can apply advanced machine learning, protein modeling, and artificial intelligence techniques to real-world drug discovery challenges that may contribute to faster and more effective scientific development.
- International Career Opportunities: Eligible international professionals may benefit from potential visa sponsorship pathways, including O-1, H-1B, J-1, TN, and other employment-based options, depending on individual circumstances.
- Advanced AI Research Environment: The role provides opportunities to work with technologies including ESM, AlphaFold-family models, RFdiffusion, ProteinMPNN, active learning, and other modern computational approaches.
- Strong Professional Growth and Ownership: Researchers can take responsibility for important projects, work with experienced scientists, receive direct feedback, and expand their influence as they demonstrate strong judgment and results.
- Comprehensive Employee Benefits: Employees may receive healthcare coverage, retirement and savings plans, a monthly wellness budget, healthy team dinners, and potential equity opportunities designed to support overall wellbeing and long-term career development.
Visa Sponsorship for International Researchers:
International researchers in computational biology and machine learning who want to work in the US may find this opportunity very intriguing.
Capable claims to have a multinational team that includes representatives from Canada, Pakistan, Germany, Austria, Switzerland, China, India, and the United States and to provide visa sponsorship when necessary.
However, not every candidate will be sponsored for a visa. The position, credentials, immigration category, and any applicable U.S. criteria may all affect eligibility. During the hiring process, candidates should validate their specific circumstances with Capable.
Why Consider This Machine Learning Researcher Opportunity?
One of the most appealing aspects of this role is the opportunity to connect machine learning research with a problem that has real-world importance.
Drug discovery is complex, expensive, and highly dependent on experimentation. Better computational tools can help researchers evaluate possibilities, prioritize experiments, analyze biological information, and make more informed decisions.
As a Machine Learning Researcher at Capable, your work would not simply be measured by whether a model performs well on a benchmark. You may also be asked whether the technology helps scientists make better decisions and whether it meaningfully accelerates drug development.
That makes the position particularly attractive to researchers who want their technical skills to contribute to something beyond a conventional software or research project.
A Career With Meaningful Scientific Impact
A career in machine learning can take many directions, but some opportunities provide a rare chance to work directly on problems connected to human health and scientific discovery.
At Capable, researchers can work closely with scientists, learn from experimental results, challenge assumptions, and improve computational methods through real-world feedback.
For ambitious researchers, that environment can be both demanding and rewarding. You are expected to think independently, but you are also surrounded by people who care deeply about scientific quality and practical results.
The opportunity may suit someone who does not want to simply maintain existing systems but instead wants to help decide what should be built, why it matters, and how its impact should be measured.
Application Process for Machine Learning Researcher Jobs in USA:
Capable describes its recruitment process as fast, transparent, and personal.
The process may include:
- Initial Application: Submit your application and research background.
- First Phone Screen: Discuss your experience, interests, and potential fit.
- Second Phone Screen: Explore your technical and research experience in greater depth.
- In-Person Work Trial: Successful candidates may complete an in-person work trial in San Francisco designed to give both sides a realistic understanding of working together.
The company encourages candidates who are interested but do not perfectly match every requirement to consider applying.
Who Should Apply?
This position may be a strong fit for a researcher who enjoys combining technical depth with scientific curiosity.
You may particularly enjoy this role if you like:
- Solving open-ended research problems.
- Building and testing new machine learning approaches.
- Working with biological data.
- Exploring protein and molecular models.
- Collaborating with experimental scientists.
- Learning quickly from unsuccessful experiments.
- Taking ownership of research projects.
- Connecting computational predictions with real-world outcomes.
- Working in a fast-moving early-stage company.
- Contributing to the future of AI-powered drug discovery.
Conclusion:
As artificial intelligence advances in scientific study, machine learning researcher positions are becoming more and more crucial. Instead of addressing AI as a separate technical function, this Capable opportunity integrates powerful machine learning with computational biology and drug discovery.
The position may offer more than just competitive compensation to the suitable researcher. It can offer the chance to collaborate with seasoned scientists, hone research judgment, take charge of worthwhile initiatives, and support mechanisms intended to hasten the development of novel medications.
This role would be worth looking into if you are an aspirational machine learning researcher in San Francisco or if you are an overseas applicant looking into U.S. prospects with possible visa sponsorship and you are enthusiastic about the nexus of AI, biology, and drug development.
What skills help candidates secure machine learning research positions?
Strong research judgment, machine learning expertise, programming ability, data analysis, experimentation, and scientific curiosity are valuable. Experience with protein models, active learning, multimodal systems, biological datasets, computational drug discovery, and production AI platforms can provide additional advantages.
Are machine learning researcher positions usually remote or onsite?
Work arrangements depend on the employer and specific position. This Capable opportunity is full-time and onsite in San Francisco, California. Candidates should carefully review each vacancy because research collaboration, laboratory access, security requirements, and team workflows may influence workplace expectations.
How can international candidates apply for these positions?
International candidates should prepare a research-focused resume, highlight relevant projects, explain technical achievements, and verify sponsorship availability before applying. Strong applications should demonstrate practical machine learning expertise, scientific reasoning, measurable results, and enthusiasm for solving challenging research problems.
